Imputation of missing gas permeability data for polymer membranes using machine learning

<a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a>

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Designing exceptional gas-separation polymer membranes using machine learning

Science Advances ◽

10.1126/sciadv.aaz4301 ◽

2020 ◽

Vol 6 (20) ◽

pp. eaaz4301 ◽

Cited By ~ 4

Author(s):

J. Wesley Barnett ◽

Connor R. Bilchak ◽

Yiwen Wang ◽

Brian C. Benicewicz ◽

Laura A. Murdock ◽

...

Keyword(s):

Machine Learning ◽

Gas Separation ◽

Gas Permeability ◽

Small Body ◽

Polymer Membranes ◽

Polymer Membrane ◽

Experimental Investigations ◽

Separation Performance ◽

Empirical Observation ◽

Membrane Design

The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, we trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit. We used a limited set of experimental gas permeability data for six different gases in ~700 polymeric constructs that have been measured to date to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties. To test the algorithm’s accuracy, we synthesized two of the most promising polymer membranes predicted by this approach and found that they exceeded the upper bound for CO2/CH4 separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.

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Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

10.26434/chemrxiv.13124993 ◽

2020 ◽

Author(s):

Qi Yuan ◽

Mariagiulia Longo ◽

Aaron Thornton ◽

Neil B. McKeown ◽

Bibiana Comesana-Gandara ◽

...

Keyword(s):

Machine Learning ◽

Energy Efficient ◽

Missing Values ◽

Gas Permeability ◽

Polymer Membranes ◽

Gas Separations ◽

Initial Stage ◽

Sparse Features ◽

Potential Use ◽

Insight Into

<a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a>

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Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

10.26434/chemrxiv.13124993.v2 ◽

2021 ◽

Author(s):

Qi Yuan ◽

Mariagiulia Longo ◽

Aaron Thornton ◽

Neil B. McKeown ◽

Bibiana Comesana-Gandara ◽

...

Keyword(s):

Machine Learning ◽

Energy Efficient ◽

Missing Values ◽

Gas Permeability ◽

Polymer Membranes ◽

Gas Separations ◽

Initial Stage ◽

Sparse Features ◽

Potential Use ◽

Insight Into

<a>Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.</a>

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A Machine Learning Methodology for Rock-Typing Using Relative Permeability Curves

10.2118/205989-ms ◽

2021 ◽

Author(s):

Carlos Esteban Alfonso ◽

Frédérique Fournier ◽

Victor Alcobia

Keyword(s):

Machine Learning ◽

Capillary Pressure ◽

Relative Permeability ◽

Fractional Flow ◽

Flow Curves ◽

Rock Types ◽

Rock Typing ◽

Relative Permeability Curves ◽

Permeability Data

Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.

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Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

10.26434/chemrxiv-2021-p4g7z ◽

2021 ◽

Author(s):

Jason Yang ◽

Lei Tao ◽

Jinlong He ◽

Jeffrey McCutcheon ◽

Ying Li

Keyword(s):

Machine Learning ◽

Polymer Membranes ◽

Polymeric Membranes ◽

Gas Separations ◽

Separation Performance ◽

High Fidelity ◽

Environmental Implications ◽

Separation Membranes ◽

Gas Separation Membranes ◽

Dynamics Simulations

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. We interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively, orders of magnitude higher than currently available polymeric membranes. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

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Permeability of N, P, K-fertilizer nutrient and water vapor through PLA, PLA/PS, and PLA/HA membranes

Polish Journal of Chemical Technology ◽

10.2478/pjct-2020-0040 ◽

2020 ◽

Vol 22 (4) ◽

pp. 61-68

Author(s):

Xiaonan Deng ◽

Sihong Ye ◽

Kun Liu ◽

Changfeng Li ◽

Fangzhi Liu ◽

...

Keyword(s):

Lactic Acid ◽

Water Vapor ◽

Humic Acid ◽

Coating Thickness ◽

Polymer Membranes ◽

Poly Lactic Acid ◽

K Fertilizer ◽

Coated Fertilizer ◽

Permeability Data

AbstractTo collect permeability data and establish its database of fertilizer nutrients and water vapor through different polymer membranes for the development of polymer-coated fertilizer, the permeabilities of N-, P-, and K-nutrient from saturated aqueous of urea, NaH2PO4 and KCl solution and the permeability of water vapor through the membranes of poly lactic acid (PLA), its blends with polystyrene (PS), and its composites with humic acid (HA) particles were determined experimentally at the temperatures of 288, 298, and 308 K, respectively. The effects of the addition of PS and HA particles, temperature, and coating thickness on the permeability of fertilizer nutrient and water vapor were investigated. It was found that the addition of PS and HA increased the permeability for both the fertilizer nutrients and water vapor. The increase in temperature raised the permeability of N-, P-, and K-nutrient while decrease the permeability of water vapor in the range studied.

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Interpretation of multi-scale permeability data through an information theory perspective

Hydrology and Earth System Sciences ◽

10.5194/hess-24-3097-2020 ◽

2020 ◽

Vol 24 (6) ◽

pp. 3097-3109

Author(s):

Aronne Dell'Oca ◽

Alberto Guadagnini ◽

Monica Riva

Keyword(s):

Information Theory ◽

Mutual Information ◽

Information Content ◽

Gas Permeability ◽

Measurement Scale ◽

Measurement Scales ◽

Multi Scale ◽

Multivariate Mutual Information ◽

Permeability Data

Abstract. We employ elements of information theory to quantify (i) the information content related to data collected at given measurement scales within the same porous medium domain and (ii) the relationships among information contents of datasets associated with differing scales. We focus on gas permeability data collected over Berea Sandstone and Topopah Spring Tuff blocks, considering four measurement scales. We quantify the way information is shared across these scales through (i) the Shannon entropy of the data associated with each support scale, (ii) mutual information shared between data taken at increasing support scales, and (iii) multivariate mutual information shared within triplets of datasets, each associated with a given scale. We also assess the level of uniqueness, redundancy and synergy (rendering, i.e., information partitioning) of information content that the data associated with the intermediate and largest scales provide with respect to the information embedded in the data collected at the smallest support scale in a triplet. Highlights. Information theory allows characterization of the information content of permeability data related to differing measurement scales. An increase in the measurement scale is associated with quantifiable loss of information about permeability. Redundant, unique and synergetic contributions of information are evaluated for triplets of permeability datasets, each taken at a given scale.

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